Path Flow Estimation Using Time Varying Coefficient State Space Model

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.